R Notebook

# Load Packages
library(crosstalk)

## Warning: package 'crosstalk' was built under R version 4.1.3

library(tidyverse)

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## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --

## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.6     v dplyr   1.0.8
## v tidyr   1.2.0     v stringr 1.4.0
## v readr   2.1.2     v forcats 0.5.1

## Warning: package 'ggplot2' was built under R version 4.1.3

## Warning: package 'tibble' was built under R version 4.1.3

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## Warning: package 'readr' was built under R version 4.1.3

## Warning: package 'purrr' was built under R version 4.1.3

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## Warning: package 'forcats' was built under R version 4.1.3

## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

library(ggthemes)

## Warning: package 'ggthemes' was built under R version 4.1.3

library(scales)

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## 
## Attaching package: 'scales'

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library(readxl)

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library(plotly)

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## Attaching package: 'plotly'

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Making Dataframes

cbPalette <- c('#000000', '#187898', '#E69F00', '#56B4E9', '#009E73', '#F0E442', '#0072B2', '#D55E00', '#CC79A7', '#128409', '#6517A9', '#B91036', '8FB910', '#1029B9')
x_label <- c('Under 5 Female', 'Under 5 Male', '05-09 Female', '05-09 Male', '10-14 Female', '10-14 Male', '15-19 Female', '15-19 Male', '20-24 Female', '20-24 Male', '25-29 Female', '25-29 Male', '30-34 Female', '30-34 Male', '35-39 Female', '35-39 Male', '40-44 Female', '40-44 Male', '45-49 Female', '45-49 Male', '50-54 Female', '50-54 Male', '55-59 Female', '55-59 Male', '60-64 Female', '60-64 Male', '65-69 Female', '65-69 Male', '70-74 Female', '70-74 Male', '75-79 Female', '75-79 Male', '80-84 Female', '80-84 Male', '85 and over Female', '85 and over Male')
counties <- c('Anson', 'Cabarrus', 'Catawba', 'Chester', 'Cleveland', 'Gaston', 'Iredell', 'Lancaster', 'Lincoln', 'Mecklenburg', 'Rowan', 'Stanly', 'Union', 'York')
attainment_lvl <- c('Highest Degree: Less than a High School Diploma', 'Highest Degree: High School Diploma', 'Highest Degree: Some College, No Degree', "Highest Degree: Associate's Degree", "Highest Degree: Bachelor's Degree", "Highest Degree: Graduate or Professional Degree")
foreign_detail <- c('Foreign-Born: Africa', 'Foreign-Born: Asia', 'Foreign-Born: Europe', 'Foreign-Born: Latin America', 'Place of Birth Total')

countypop <- rbind(read_csv("cc-est2019-agesex-37.csv", show_col_types = F),
                   read_csv("cc-est2019-agesex-45.csv", show_col_types = F)) %>%
  select(-SUMLEV, -STATE, -COUNTY) %>%
  mutate(CTYNAME = gsub(' County', '', CTYNAME),
         YEAR = as.integer(YEAR + 2007)) %>%
  filter(CTYNAME %in% counties, YEAR >= 2010,
         !(STNAME == 'South Carolina' & CTYNAME == 'Union')) %>%
  distinct()
# Year 3 is 2010, Year 12 is 2019

# Making Charlotte Region
cr <- countypop[1:10,] %>%
  mutate(CTYNAME = 'Charlotte Region')
for(i in 4:length(colnames(countypop))) {
  for(j in 1:10){
    cr[j,i] <- sum((countypop %>% filter(YEAR == j+2009))[i])
  }
}

ggplot(data = countypop %>% filter(YEAR == 2019)) +
  geom_col(aes(CTYNAME, POPESTIMATE, fill = CTYNAME), show.legend = F) +
  scale_y_continuous(labels = comma) +
  scale_fill_manual(values = cbPalette) +
  coord_flip()

shared_pop <- SharedData$new(countypop)

bscols(widths = c(3, NA),
       list(
         filter_select('cp_year', label = 'Select Year', shared_pop, ~YEAR)),
       plot_ly(shared_pop, x = ~CTYNAME, y = ~POPESTIMATE, type = 'bar')
)

Select Year